Francis Sofia Biju, Prakash Verma Jai
Department of Computer Science and Engineering, Institute of Technology, Nirma University, Gujarat, India.
Department of Computer Engineering, NMIMS, MPSTME, Mumbai, India.
Front Neuroinform. 2025 Jan 7;18:1507217. doi: 10.3389/fninf.2024.1507217. eCollection 2024.
The prevalence of age-related brain issues has risen in developed countries because of changes in lifestyle. Alzheimer's disease leads to a rapid and irreversible decline in cognitive abilities by damaging memory cells.
A ResNet-18-based system is proposed, integrating Depth Convolution with a Squeeze and Excitation (SE) block to minimize tuning parameters. This design is based on analyses of existing deep learning architectures and feature extraction techniques. Additionally, pre-trained ResNet-18 models were created with and without the SE block to compare ROC and accuracy values across different hyperparameters.
The proposed model achieved ROC values of 95% for Alzheimer's Disease (AD), 95% for Cognitively Normal (CN), and 93% for Mild Cognitive Impairment (MCI), with a maximum test accuracy of 88.51%. However, the pre-trained model with SE had 93.26% accuracy and ROC values of 98%, 99%, and 98%, while the model without SE had 94%, 97%, and 94% ROC values and 92.41% accuracy.
Collecting medical data can be expensive and raises ethical concerns. Small data sets are also prone to local minima issues in the cost function. A scratch model that experiences extensive hyperparameter tuning may end up being either overfitted or underfitted. Class imbalance also reduces performance. Transfer learning is most effective with small, imbalanced datasets, and pre-trained models with SE blocks perform better than others. The proposed model introduced a method to reduce training parameters and prevent overfitting from imbalanced medical data. Overall performance findings show that the suggested approach performs better than the state-of-the-art techniques.
由于生活方式的改变，发达国家与年龄相关的脑部问题患病率有所上升。阿尔茨海默病通过损害记忆细胞导致认知能力迅速且不可逆转地下降。
提出了一种基于ResNet - 18的系统，将深度卷积与挤压激励（SE）块相结合以最小化调优参数。该设计基于对现有深度学习架构和特征提取技术的分析。此外，创建了带有和不带有SE块的预训练ResNet - 18模型，以比较不同超参数下的ROC和准确率值。
所提出的模型在阿尔茨海默病（AD）上的ROC值为95%，在认知正常（CN）上为95%，在轻度认知障碍（MCI）上为93%，最大测试准确率为88.51%。然而，带有SE的预训练模型准确率为93.26%，ROC值分别为98%、99%和98%，而没有SE的模型ROC值分别为94%、97%和94%，准确率为92.41%。
收集医学数据可能成本高昂且引发伦理问题。小数据集在成本函数中也容易出现局部极小值问题。经历大量超参数调优的从头开始训练的模型最终可能会出现过拟合或欠拟合。类别不平衡也会降低性能。迁移学习在小的、不平衡的数据集上最为有效，带有SE块的预训练模型比其他模型表现更好。所提出的模型引入了一种减少训练参数并防止不平衡医学数据过拟合的方法。总体性能结果表明，所建议的方法比现有技术表现更好。
